Introduction to Python for Science: – Overview of numpy, scipy and matplotlib

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Introduction to Python for Science: – Overview of numpy, scipy and matplotlib

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pybcn-python4science

Pybcn meetup talk on "Introduction to Python for Science: Overview of numpy, scipy and matplotlib"

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Introduction to Python for Science:

Overview of numpy, scipy and matplotlib

By Christine Doig @ch_doig

About you
  • Background:
    • Software/Developers/CS?
    • Scientist?
    • Engineers?
    • Business?
    • Other?
  • Experience:
    • Matlab?
    • Programming languages?
Objectives
  • Why Python for Science?
  • Python Scientific Computing Resources
  • Overview of Numpy, Scipy and Matplotlib libraries
Python for Science

Why Python for science?

  • Simplicity and ease of writing code in Python
  • Open Source
  • Reproducibility: Sharing results, code and "research paper" with IPython notebook
  • Run time code: C or FORTRAN can be interfaced with Python
  • Modules for specific tasks can be easily developed and distributed
Scientific Computing in Python

Scipy: Python-based ecosystem of open-source software for mathematics, science, and engineering.

http://www.scipy.org/

Annual conferences for scientists using Python:

  • SciPy: Austin, Texas, July 6-12.
  • EuroSciPy: Cambridge, UK, Aug. 27-31.
Python for Science

Most Generally Useful Modules

http://www.scipy.org/

Python for Science

Other modules

https://wiki.python.org/moin/NumericAndScientific

Examples:

  • Biology: Biopython
  • Astrology: astropy
  • Graphs/Network analysis: graph-tool
  • Machine Learning: Scikit-learn
  • Natural Language Processing: NLTK
Numpy

Numpy

Provides powerful numerical arrays objects, and routines to manipulate them. http://www.numpy.org/

Tutorial:

http://wiki.scipy.org/Tentative_NumPy_Tutorial http://scipy-lectures.github.io/

Demo

Scipy

Scipy

Tools for a number of common problems in numerical analysis:

  • Basic functions
  • Special functions (scipy.special)
  • Integration (scipy.integrate)
  • Optimization (scipy.optimize)
  • Interpolation (scipy.interpolate)
  • Fourier Transforms (scipy.fftpack)
  • Signal Processing (scipy.signal)
  • Linear Algebra (scipy.linalg)
  • Sparse Eigenvalue Problems with ARPACK
  • Compressed Sparse Graph Routines scipy.sparse.csgraph
  • Spatial data structures and algorithms (scipy.spatial)
  • Statistics (scipy.stats)
  • Multidimensional image processing (scipy.ndimage)
  • File IO (scipy.io)
  • Weave (scipy.weave)

Demo

Matplotlib

Matplotlib

Python Package for 2D graphics

Note: matplotlib.pyplot is a collection of command style functions that make matplotlib work like MATLAB